Unsupervised representation learning in deep reinforcement learning: A review
This review addresses the problem of learning abstract representations of the measurement
data in the context of Deep Reinforcement Learning (DRL). While the data are often …
data in the context of Deep Reinforcement Learning (DRL). While the data are often …
Automatic noise filtering with dynamic sparse training in deep reinforcement learning
Tomorrow's robots will need to distinguish useful information from noise when performing
different tasks. A household robot for instance may continuously receive a plethora of …
different tasks. A household robot for instance may continuously receive a plethora of …
Deep reinforcement learning based robot navigation in dynamic environments using occupancy values of motion primitives
This paper presents a Deep Reinforcement Learning based navigation approach in which
we define the occu-pancy observations as heuristic evaluations of motion primitives, rather …
we define the occu-pancy observations as heuristic evaluations of motion primitives, rather …
HypeRL: Parameter-Informed Reinforcement Learning for Parametric PDEs
In this work, we devise a new, general-purpose reinforcement learning strategy for the
optimal control of parametric partial differential equations (PDEs). Such problems frequently …
optimal control of parametric partial differential equations (PDEs). Such problems frequently …
Parametric PDE Control with Deep Reinforcement Learning and Differentiable L0-Sparse Polynomial Policies
N Botteghi, U Fasel - arXiv preprint arXiv:2403.15267, 2024 - arxiv.org
Optimal control of parametric partial differential equations (PDEs) is crucial in many
applications in engineering and science. In recent years, the progress in scientific machine …
applications in engineering and science. In recent years, the progress in scientific machine …
Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models
M Tomasetto, F Braghin, A Manzoni - arXiv preprint arXiv:2412.09942, 2024 - arxiv.org
Continuous monitoring and real-time control of high-dimensional distributed systems are
often crucial in applications to ensure a desired physical behavior, without degrading …
often crucial in applications to ensure a desired physical behavior, without degrading …
PixelBytes: Catching Unified Representation for Multimodal Generation
F Furfaro - arXiv preprint arXiv:2410.01820, 2024 - arxiv.org
This report presents PixelBytes, an approach for unified multimodal representation learning.
Drawing inspiration from sequence models like Image Transformers, PixelCNN, and Mamba …
Drawing inspiration from sequence models like Image Transformers, PixelCNN, and Mamba …
[图书][B] Explainable and Interpretable Reinforcement Learning for Robotics
All rights are solely and exclusively licensed by the Publisher, whether the whole or part of
the material is concerned, specifically the rights of translation, reprinting, reuse of …
the material is concerned, specifically the rights of translation, reprinting, reuse of …
Enhancing Motion Planning Efficiency in Dynamic Environments Through Advanced Algorithms for Mobile Robots
NÜ Akmandor - 2024 - search.proquest.com
Robots are on the verge of becoming integral components of our domestic environments as
personal assistants. Although their present functions are confined to specific tasks within …
personal assistants. Although their present functions are confined to specific tasks within …
PixelBytes: Catching Unified Embedding for Multimodal Generation
F Furfaro - 2024 - hal.science
This report introduces PixelBytes Embedding, a novel approach for unified multimodal
representation learning. Our method captures diverse inputs in a single, cohesive …
representation learning. Our method captures diverse inputs in a single, cohesive …